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A Uniform Cost-Comparison Metric for GHG Reduction Measures in the Electric Sector

Gigio Sakota, Andrew Dugowson

Abstract There are many programs to procure technologies with GHG benefits, and each of these programs has its own cost-effectiveness calculation. However, these calculations are tailored to their specific program and may therefore include different costs and benefits and operate from different assumptions. As a result, GHG cost-effectiveness numbers from different programs are not directly comparable. This paper outlines a conceptual framework for a uniform cost comparison, on a $/ton basis, of different approaches to long-term GHG reductions. First, the paper discusses the characteristics of a fair comparison between GHG-reduction measures. This includes a standardized calculation of the net cost of each measure, and recognition of emissions impacts outside the electric sector. The latter is important in areas such as electric vehicles, which may trade an increase in electric sector emissions for greater reductions in transportation sector emissions. Second, this paper illustrates the importance of these considerations by applying them to two published studies. The paper evaluates the assumptions embedded in two reports that contain the information necessary to perform abatement cost calculations. As a result, the paper shows that the implied abatement costs from the two studies were not comparable due to substantial differences in methodology. --

Gigio Sakota

Integrated Planning & Analysis

Southern California Edison Company

Gigio.Sakota@sce.com

Andrew Dugowson

Energy & Environmental Policy

Southern California Edison Company

Andrew.Dugowson@sce.com

This paper represents the views of the authors, and does not necessarily reflect the views of the

Southern California Edison Company.

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Introduction

California has set ambitious goals to largely decarbonize its economy by 2050. For instance, in 2015

Governor Brown led an international coalition to agree to aggressive 2050 goals, pursuing emission

targets that are 80 to 95 percent below 1990 levels and/or achieving annual per capita emissions of 2

metric tons. Prior to that, he set an ambitious 2030 target for greenhouse gas (GHG) levels in the state.

His predecessor, Governor Schwarzenegger, signed legislation that adopted a target of achieving 1990

GHG levels by 2020, enforced through a wide range of GHG-reduction measures and a cap-and-trade

mechanism.

These targets are just part of the story. California has adopted a number of policies whose primary or

secondary goal is to reduce the carbon intensity of the state economy. For this paper, we focus on the

policies and programs that affect the electric sector. These include a wide variety of programs, such as

legislative procurement mandates (e.g., the Renewables Portfolio Standard and the energy storage

procurement target1), indirect subsidies (e.g., the Net Energy Metering retail tariff), and utility-

administered incentives (e.g., energy efficiency programs).

This paper addresses the lack of a standardized metric across program areas that fairly compares each

program’s cost-effectiveness at abating greenhouse gas (GHG). While there are many programs that

reduce GHG emissions, it is challenging to compare their cost-effectiveness. For example, the data and

methodology are not readily available to determine whether energy efficiency or renewable energy is

more cost-effective at reducing GHG emissions.

This type of cost-effectiveness analysis will be important as California continues to reduce its GHG

footprint through a coordinated set of state agency policy actions (such as building and appliance

efficiency standards), taxes, subsidies, legislative programs such as the Renewables Portfolio Standard,

and economic incentives embedded within its cap-and-trade program. All else equal, it is important to

pursue carbon reductions in the most cost-effective manner possible, as that will allow for the largest

carbon reductions with same or lower cost and investment. Cost containment and investment

prioritization are important metrics by which the success of California’s climate policies should be

judged.

Overview of the Analysis

This paper is divided into two main sections.

The first section discusses the characteristics of a fair comparison between individual GHG-reduction

measures. “GHG-reduction measure” is a deliberately broad term that includes any activity, technology,

1 The Renewables Portfolio Standard (RPS) was established in 2002 under Senate Bill 1078, accelerated in 2006

under Senate Bill 107 and expanded in 2011 under Senate Bill 2. The California Public Utilities Commission (CPUC) adopted an Energy Storage procurement target in Decision 13-10-040, following Assembly Bill 2514 signed into law September 2010.

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or program that reduces GHG2. This could range from energy efficiency to transportation electrification

to time-of-use rates that shift energy use patterns. A fair comparison includes a standardized calculation

of the net cost of each measure, and recognition of emission impacts outside the electric sector.

Additionally, this section discusses considerations for a comparison of larger scale measures, or

portfolios of measures. This is important because many measures are interdependent. For example, as

the carbon intensity of the electricity supply goes down, energy efficiency becomes less effective as a

carbon abatement tool.

The second section illustrates the importance of these considerations by applying them to two

published studies. We evaluate the assumptions embedded in two reports that contain the information

necessary to perform abatement cost calculations. As a result, we show that the implied abatement

costs from the two studies were not comparable due to substantial differences in methodology.

Characteristics of a Fair Comparison

The natural unit for the cost-effectiveness of carbon-reduction measure is dollars per ton ($/ton): the

net cost of a measure divided by the number of tons the measure abates. However, the methodology to

calculate the cost and the total amount of carbon abatement impact is not obvious when abatement

occurs over multiple years, when abatement impacts are dependent on other abatement programs, and

when distributional impacts affect who bears program costs. The problem is compounded for carbon-

reduction measures which impact multiple sectors of the economy, such as transportation

electrification.

When comparing the cost-effectiveness of any GHG-abatement measures, a common metric for

comparison of multi-year measurers is their $/ton abatement costs. To ensure a fair comparison, it is

important to develop a standard approach to $/ton calculations. Consistency is important across all

facets of the analysis, but this paper highlights the following areas for discussion:

1. Choice of Cost Test

2. Counterfactual Assumptions

3. Monetized Externalities

4. Discounting Abated GHG

5. Analysis Timeframe

6. GHG Accounting for Abatement Calculation: Direct vs Lifecycle Emissions

7. GHG Reductions Attribution to Indirect Incentives

8. Effect of Large-Scale Measures on the Grid

9. Sequencing of Abatement Measures and Perceived Abatement Effectiveness

Each of these areas is addressed in turn.

2 This paper assumes that all GHG emissions are expressed in tons of carbon dioxide equivalent, or CO2e. In this

paper, “GHG” and “carbon” are used interchangeably, where both refer to the full scope of gasses with global warming potential.

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1. Choice of Cost Test

While the California Standard Practice Manual3 is focused on demand-side programs and

projects, the conclusions it draws are broader: the scope of costs and benefits in a cost-

effectiveness calculation vary depending on the perspective from which the analysis is

performed. For example, the Program Administrator Cost (PAC) test is scoped to evaluate the

costs incurred by the program administrator, while the Societal Cost Test (SCT)4 attempts to

capture the value of a program’s impact on the society as a whole.

As an example, consider a California utility that conducts a solicitation for renewable energy

with no technology restrictions, and chooses to evaluate projects using only the Program

Administrator Cost test.

In California, there is currently a robust market for no-money-down solar leases that are made

financially viable by the Net Energy Metering (NEM) tariff.5 As the costs of the project are

entirely covered by the combination of a federal tax credit and the NEM retail tariff, rooftop

solar is financially viable even before the renewable energy aspect is monetized. This contrasts

to large-scale renewables projects which typically finance new construction through wholesale

Power Purchase Agreements.

Due to this discrepancy,6 the owners of the rooftop behind-the-meter systems would be able to

price their bids for renewable energy substantially lower than the large-scale wholesale

developers. If the utility were to rely solely on the PAC test, the rooftop solar projects would

likely win a place in the solicitation. However, this test ignores both the higher capital cost of

rooftop solar (as compared to large-scale solar) and the cross-subsidy created by the NEM tariff.

These aspects would be captured by the Total Resource Cost test and the Ratepayer Impact

Measure, respectively.

This highlights that there is no single “correct” cost test, as each test evaluates a project from a

different perspective. This paper does not endorse a particular test; there are scenarios where

each test is appropriate. However, for the purpose of comparison, it is important that measures

being compared are all evaluated using the same test(s). It would give misleading results to

compare the output of a PAC test for energy efficiency to an SCT for renewable energy.

2. Counterfactual Assumptions

3 The CPUC’s Standard Practice Manual is available online at http://www.cpuc.ca.gov/NR/rdonlyres/004ABF9D-

027C-4BE1-9AE1-CE56ADF8DADC/0/CPUC_STANDARD_PRACTICE_MANUAL.pdf (Accessed 6/18/15) 4 Societal cost test is a variation of the Total Resource Cost (TRC) test where the effects of externalities are

included, and tax benefits are excluded because they are categorized as wealth transfers. 5 “Market Share for Leasing Residential Solar to Peak in 2014”, Greentech Media,

http://www.greentechmedia.com/articles/read/Market-Share-for-Leasing-Residential-Solar-to-Peak-in-2014 (Accessed 6/18/15) 6 The discrepancy arises from the fact that retail energy rates are higher than wholesale energy rates, as they

include delivery charges for transmission, distribution and metering services.

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Most cost-benefit calculations rely on a significant number of counterfactual assumptions such

as energy prices, capacity value, etc. For example, the benefits of energy efficiency or renewable

energy include the avoided cost of purchased power (energy) and capacity to meet system

needs7, the costs of transportation electrification include an associated increase in electricity

consumption.

The important factor here is to ensure that all cost-effectiveness calculations rely on the same

set of counterfactual assumptions; that is, they should use the same power price forecast,

capacity value forecast, etc.

It is equally important to note that this practice (i.e., using the same set of counterfactual

assumptions to compare two measures) reflects an embedded assumption that both measures

do not affect the global system characteristics. In other words, the analysis assumes that each

measure is treated as a marginal resource. However, if the measures compared are large

enough to impact these forecasts, as discussed in part 8 below, then the recommendation is to

establish base assumptions and develop consistent forecasts (counter-factuals) based on them,

considering the measures being analyzed.

3. Monetized Externalities

There must be consistency in the externalities that each analysis monetizes. Consider an

example where we compare electric vehicles to renewable energy. If we assign value to abated

NOx or SOx emissions as a result of reduced vehicle fuel use, then it is important to identify and

monetize any NOx or SOx reductions due to the renewable energy. If those externalities cannot

be monetized, they should at least be explicitly acknowledged.

4. Discounting Abated GHG

One of the key questions to address in comparing abatement measures is whether a ton of GHG

saved in 2015 is the same as a ton saved in 2016 (or some other future year). In other words,

should we discount the GHG savings similar to how we discount the cash flows8? In general, the

preferred approach for such externalities would be to rely on a fundamental market forecast

that would allow us to assign a dollar value to the abated GHG, and then treat abated GHG the

same way as any other cash flow. However, this paper does not monetize GHG.

In that case, we recommend thinking of the GHG abatement as a compliance instrument, where

we can implement a GHG abatement measure ourselves, or purchase a compliance instrument

from the market, at an unknown price. Intuitively, in a simplified scenario, the compliance price

7 Avoided capacity value is usually benchmarked to avoided purchases or additions needed to meet Resource

Adequacy and/or Planning Reserve Margin reliability requirements (i.e. system needs). 8 For the purpose of this paper, we assume that the environmental impact of emitting (or avoiding) GHG in 2015 is

equivalent to emitting (or avoiding) it in a subsequent year, and focus on the economic principles involved.

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should escalate at some rate, greater than the inflation, to account for time value of money.9

Therefore, it is appropriate to discount the future GHG abatement benefits, similar to how we

discount monetary costs and benefits.

Here is an example of where that makes a difference: consider two GHG reduction measures.

The first – say, carbon sequestration – is expected to abate 10 million tons of GHG in its first

year of operation, but none afterward. The second – planting a small forest – is expected to

reduce 250 thousand tons of GHG per year for one hundred years. While the reforestation is

expected to eventually remove more carbon from the atmosphere than the sequestration

project, the forest will do so at a much slower pace. Which abatement is more valuable?

There are multiple perspectives one could take to this issue. First: GHG is a global, enduring

pollutant, and from a long-term perspective there is likely no difference between GHG emitted

in 2015 and 2016, or even 2015 and 2025. Accordingly, analysis would use the nominal total of

GHG emissions. Second: the long-term GHG reductions are less valuable because they provide

less assistance in meeting near-term annual emissions targets. GHG abated in 2125 does not

help with 2030 goals. Third: analogous to the way that financial analysis discounts future cash

flows, it is appropriate to discount future GHG abatement.

While these are just some of the approaches that one could take to discounting, this paper

endorses discounting the amount of emissions, treating them as a compliance instrument rather

than simply an accounting of global emissions. So the comparison to be made between two

measures is the Net Present Value (NPV) over n years, discounted at rate r:

∑Cost1,𝑛(1 + 𝑟)𝑛

𝑁

𝑛=1

∑GHG1,𝑛(1 + 𝑟)𝑛

𝑁

𝑛=1

⁄ 𝑣𝑠.∑Cost2,𝑛(1 + 𝑟)𝑛

𝑁

𝑛=1

∑GHG2,𝑛(1 + 𝑟)𝑛

𝑁

𝑛=1

⁄

For example, let us compare two illustrative measures: an energy efficiency (EE) measure, with

costs of $1,000 in year 1, and benefits of 2 ton/year over 10 years, to a transportation subsidy,

with costs of $600 in years 1 & 2, and benefits of 10 ton/year over 2 years. One may be drawn to

preferring the EE measure, as having lower costs ($1,000 vs $1,200 total, or $909 vs. $1,041

NPV, discounted to year zero at a 10% rate10) for the same total abated GHG of 20 ton.

However, this would be erroneous, as the value of the achieved GHG reductions is not the same.

Discounting to year 0, the EE measure produces only 12.3 ton, while the transportation subsidy

produces 17.4 ton of savings in NPV terms. Dividing by NPV of cost, a transportation subsidy is a

clear winner at $60/ton of GHG abated, compared to the $74/ton for the EE measure.

9 If the price of GHG compliance was to stay flat in time, this would provide an arbitrage opportunity, where an

entity could sell GHG compliance (abatement) in present year, and buy it back the following year – getting a free loan, from which they could profit. 10

10% discount rate is used for illustrative purposes.

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5. Analysis Timeframe

Closely tied to the issue of GHG discounting is the issue of analysis timeframe. Many of the

state’s climate goals are expressed in terms of annual rates, rather than cumulative totals. For

example, California’s mid-century goal is expressed as “85 million metric tons in 2050,” rather

than “5,000 cumulative million metric tons through 2050.”

The effect of single-year targets is to focus attention on carbon reductions that occur on or

before the year in question. Returning to the example of the reforestation project, a flawed line

of thinking goes as follows: even though a forest that lives 100 years may abate 25 tons of GHG

over its lifetime, most of those emissions reductions would occur far in the future – and

therefore be of little value to an entity trying to comply with an annual target in 2030.

Therefore, the analysis should only evaluate the impact on emissions in 2030.

As indicated above, this line of logic is flawed. It is not the responsibility of a single measure to

meet annual emissions targets, but rather a portfolio of GHG-reducing measures. So any

comparison of individual measures should consider the expected GHG reductions over the

lifetimes of each measure. Additionally, the targets typically represent an emissions level that

must be sustained for the target year and beyond.

6. GHG Accounting for Abatement Calculation: Direct vs Lifecycle Emissions

For any given GHG-reduction measure, there are a range of assumptions about how far up the

value stream the measure has impact. For example, consider the use of renewable energy to

displace natural gas.11

At one end of the spectrum, there are directly avoided GHG emissions. We can quantify the

amount of natural gas that was not combusted due to renewable energy, and use that value as

the total GHG impact of renewable energy.

At the other end of the spectrum, there are lifecycle GHG emissions caused by this energy

source switch. In this approach, we quantify not just the marginal reduction in natural gas

combustion, but also the abated emissions from a marginal reduction in energy required to

transport the gas from wellhead to the generator, and the marginally reduced volume of

resource harvesting and processing activity. Likewise, we would need to capture the marginal

increase in lifecycle emissions from the renewable energy project side.

These two approaches are opposite sides of a long continuum, and can produce significantly

different abatement numbers when describing the same GHG reduction measure. As with the

other criteria, the key point is that these approaches must be consistent when comparing two

measures. One must take special care when comparing measures such as transportation

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For simplicity, the assumption here is that 1 megawatt-hour of renewable energy would displace 1 megawatt-hour of natural-gas fired (i.e., combustion turbine) energy, with no additional impacts to the electrical grid.

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electrification to renewable energy, or supply-side resources with demand-side resources such

as energy efficiency or behavioral marketing. This is especially the case when the measures

being compared have large differences in capital requirements and variable costs – which may

indicate that emissions occur upstream.12

A 2003 presentation from the Argonne National Lab shows the significant difference between

direct and lifecycle costs.13 When discussing a vehicle’s emissions impact, the phrase “tank to

wheel” or “pump to wheel” is close to what this paper calls direct emissions: it represents the

emissions from fuel combustion in a motor vehicle for power. “Well to pump” emissions

account for the upstream emissions associated with extracting, refining, and delivering the fuel

to the “pump,” so the sum of those two quantities gives “well to wheel,” or lifecycle, emissions.

Figure 1: A Comparison of Direct (“Pump to wheels”) and Upstream (“Well to Pump”)

Emissions for Various Passenger Vehicles14

Source: “Well-to-Wheels Energy and Emission Impacts of Vehicle/Fuel Systems”, Argonne National Lab

The figure above shows the dramatic difference that the inclusion of life-cycle considerations

can have on the calculated GHG emission reductions and the resulting $/ton valuation. Ignoring

life-cycle costs and emissions could lead to selection of GHG reduction measures that may

appear cost-effective, but in fact would increase overall GHG emissions.

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Presumably, a project with large capital investment and small marginal costs may have relatively large lifecycle emissions and relatively small marginal emissions, while a project with a small capital investment requirement and large variable costs may have relatively small life-cycle emissions and relatively large marginal emissions. 13

“Well-to-Wheels Energy and Emission Impacts of Vehicle/Fuel Systems”, Argonne National Laboratory, http://www.transportation.anl.gov/pdfs/TA/273.pdf (Accessed 6/18/15)

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7. Attributing Responsibility for GHG Reductions to Indirect Incentives

There are some GHG reduction measures that do not directly reduce carbon, but instead

provide incentives for third parties to change behavior in a way that reduces GHG emissions.

One example of this type of program would be rebates (i.e., subsidies) for the purchase of LED

lightbulbs. While the costs of such a program are clear (the sum of administrative and rebate

expenditures), the GHG savings may prove challenging to evaluate. Program administrators

must track both the adoption of LED lightbulbs, and then determine what portion of that

adoption is attributable to rebates. There is a substantial body of work on this topic, especially

as it relates to energy efficiency, and this paper does not further address this topic.

The situation grows in complexity when there are multiple indirect incentives that are designed

to promote the same goal. For example, there are various programs that are designed to

promote the adoption of electric vehicles (EVs): a federal tax incentive, state rebates, and

electric utilities’ proposals to build EV charging stations in public areas.

In the same manner that we evaluate a single program’s effectiveness, we must estimate the

combined measures’ impact on EV adoption rates. For example, let us assume a combination of

federal, state, and local programs increased sales by 100% from the counterfactual scenario.

Using this information, policymakers may want to compare the effectiveness of the state,

federal, and local programs. Additionally, they may want to compare a single program to

another GHG reduction measure (e.g., compare the cost-effectiveness of state rebates for EVs

and large-scale solar).

The challenge here is to disaggregate the effects of a single measure (state rebates) from the

broader portfolio (federal, state, and local measures). This process is not straightforward. The

biggest pitfall is to assign all of the increased sales to a single measure, as this greatly overstates

its effectiveness. Additionally, the impact of a portfolio of measures may be greater than the

sum of what the individual measures’ impact would be if they were introduced separately.

Because of this complexity, it may often be easier and more accurate to discuss the cost-

effectiveness of the entire portfolio, rather than individual measures.

These issues highlight important considerations for a fair comparison. The first consideration is

to identify whether the measure is part of a broader portfolio. Failing to do so can significantly

overstate a measure’s effectiveness. The second is, where possible, to avoid disaggregating the

portfolio. If it must be done, recognize the inherent uncertainty in the procedure.

8. Effect of Large-Scale Measures on the Grid

Large GHG measures can impact both the grid and each other. New resources connecting to the

grid can affect power prices, capacity values, the emissions intensity of power (i.e., tons of GHG

emitted per megawatt-hour of energy generated), etc.

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Accordingly, whenever an abatement cost calculation does not take into account the impact of

that measure on the grid, there is an embedded assumption that the resources are marginal.

However, as we begin to evaluate larger measures or portfolios of measures, this becomes an

unrealistic assumption.

A well-known example is the potential impact of the increasing penetration of solar resources,

colloquially known as the “duck curve”. Solar energy is currently highly valued as it provides

energy and capacity value during the system peak. However, as solar penetration increases, the

relative value of solar energy will decrease and it may eventually raise costs (i.e. have a negative

value) by increasing the electric system’s need for fast-response, flexible generation, or by

causing over-generation issues.

For calculation purposes, the practical question is “at what point is a measure large enough that

it can no longer be modeled as marginal?” While the answer to that question is a judgment call

outside of the scope of this paper, it must be considered. Unfortunately, estimating a resource’s

impact on the grid introduces another source of uncertainty into the analysis.

For the purpose of comparing the abatement cost-effectiveness, it is reasonable to evaluate

measures that are similar in size (where size is measured by the change in load served). This

increases the likelihood that the grid impacts of the measures are comparable, or at least the

same order of magnitude.

9. Sequencing of Abatement Measures and Perceived Abatement Effectiveness

The sequence in which measures are implemented can distort calculations of the GHG abated by

each measure, therefore it is important to focus on comparing the overall portfolios, rather than

attempting to attribute reductions to each of their components.

For example, consider a simplified example of two measures: an energy efficiency program that

will cut total energy use by 25% and a renewable energy program that would reduce the carbon

intensity of the electricity grid by 50%.

If the energy efficiency is implemented first, it will reduce energy usage (and GHG emissions) to

75% of baseline levels. The renewable energy will then reduce the carbon footprint of the

remaining energy by half, to 37.5% of baseline GHG levels.

If the renewable energy is implemented first, it will reduce carbon intensity (and therefore GHG

emissions) to 50% of baseline levels. The energy efficiency will then cut energy usage by 25% of

baseline levels, but because renewable energy has already halved its carbon intensity, the

incremental GHG reduction will only be an additional 12.5%, bringing the total reduction again

to 37.5% of baseline GHG levels.

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The only difference between the two scenarios is the sequence of the measures: the measures

are the same, and their total reductions are the same. However in the first scenario, it appears

that energy efficiency reduces GHG by 25% from the baseline, while in the second scenario it

appears to only reduce GHG by 12.5% from the baseline. These variations would greatly affect

the abatement cost calculations for each measure. Furthermore, this example is oversimplified:

both measures could be developed concurrently, and the rest of the grid would undergo

significant changes over the life of the measures.

The example shows that it is misleading to try to allocate GHG savings between two or more

measures that are working concurrently. Instead of comparing the GHG cost potential of two

large measures, it instead makes sense to compare the GHG abatement cost of two different

portfolios of measures.

A Comparison of Two Abatement Cost Calculations

This section compares two abatement cost calculations drawn from two influential and comprehensive

studies. The first study is “Investigating a Higher Renewables Portfolio Standard in California” (the “RPS

study”), which evaluated the cost and system impacts of increasing California’s Renewables Portfolio

Standard to 50%15. The second study is Phase 2 of the “California Transportation Electrification

Assessment” (the “TEA study”), a series of reports designed to investigate the impact and benefits of

transportation electrification as they relate to California’s GHG and air quality goals16. Both of these

studies were led by Energy and Environmental Economics (“E3”), a San Francisco-based consultancy.

It is important to note that the primary purpose of these studies was not to calculate the abatement

costs of renewable energy or transportation electrification. This section discusses the implied

abatement costs in each of the studies, and shows how values that at first glance appear comparable

can actually represent very different quantities.

At a high level, the RPS study simulated how the electric sector would behave under multiple scenarios,

including a 33% RPS scenario, a 40% RPS scenario, and several 50% RPS scenarios. This simulation

produced both power prices and expected GHG emissions. Using this information, E3 could compare the

scenarios and estimate the abatement costs, leading to the bar chart below. Each of the bars represents

a different scenario, where both the RPS level and resource composition vary.

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“Investigating a Higher Renewables Portfolio Standard in California”, Energy and Environmental Economics, https://ethree.com/documents/E3_Final_RPS_Report_2014_01_06_with_appendices.pdf (Accessed 6/18/15) 16

“California Transportation Electrification Assessment, Phase 2: Grid Impacts”, Energy and Environmental Economics, http://www.caletc.com/wp-content/uploads/2014/10/CalETC_TEA_Phase_2_Final_10-23-14.pdf (Accessed 6/18/15)

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The key takeaway is that the graph below shows abatement costs attributable to a portfolio of

renewable energy projects that range from $340/ton to $1020/ton.

Figure 2: Implied Cost of Carbon Abatement in 2030, Compared to a 33% RPS Scenario

Source: “Investigating a Higher Renewables Portfolio Standard in California”, E3

The TEA study does not directly calculate abatement costs, but it offers several graphs from which the

abatement cost can be inferred. Figures 3 and 4 show the cost-effectiveness of electric vehicles using

both the Total Resource Cost test and the Societal Cost Test; additional cost tests are available in the

TEA study but not shown in this paper.

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Figure 3: Total Resource Cost per Vehicle

Source: “California Transportation Electrification Assessment, Phase 2: Grid Impacts”, E3

Figure 4: Societal Cost Test, per Vehicle

Source: “California Transportation Electrification Assessment, Phase 2: Grid Impacts”, E3

These graphs show the net benefit on a per vehicle basis for passenger vehicles in California. Note that

these graphs include a monetized GHG benefit. Since the monetized value of the GHG benefit is not

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explicitly stated, we approximate from the charts above that the GHG benefit is roughly $2,000 per

vehicle. If we subtract this $2,000 from the net cost calculations shown in the charts above, then we see

that the benefit of electric vehicles, exclusive of the carbon benefit, is roughly $3,000 / vehicle using the

Total Resource Cost test, and $4,000 / vehicle using the Societal Cost Test.17

Additionally, the TEA Phase 2 does not explicitly state the amount of GHG abated by the vehicles.

However, this information is available in the Phase 1 of the TEA study.18 Using the values reported in

Phase 1 we can calculate the expected annual emissions reductions for the typical electric vehicle in

2030, which is approximately 2.5 tons per vehicle per year, or 50 nominal tons over its 20-year

lifetime19. At a 10% discount rate, these 50 nominal tons are discount to 23.5 tons in 2014.

Combining these values (the net cost per vehicle and the emissions savings per vehicle) yield

approximate abatement costs of -$170 (TRC) to -$130 (SCT) per ton abated over the lifetime of the

vehicle, in 2014 dollars. In other words, depending on the cost test, electric vehicles are expected to

provide between $130 and $170 in benefits per ton of GHG they abate over their lifetime.20

This provides preliminary abatement cost number from each study, as shown in table below:

Table 1: Abatement Cost Comparison: Renewable Energy and Electric Vehicles (in 2014 $)

Electric Vehicles Renewable Energy

Low $350 -$170

High $1050 -$130

However, these costs are not comparable. In Table 2, we evaluate both of these calculations using the

considerations identified earlier in the paper and examine how this affects the studies’ comparability.

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For the Total Resource Cost test, ($4,977 – $2,000) = $2,977 which is approximately $3,000. For the Total Resource Cost test ($6,166 – $2,000) = $4,166 which is approximately $4,000. 18

“California Transportation Electrification Assessment, Phase 1: Final Report”, Energy and Environmental Economics, http://www.caletc.com/wp-content/uploads/2014/09/CalETC_TEA_Phase_1-FINAL_Updated_092014.pdf (Accessed 6/18/15) 19

The TEA Study Phase 1 has tables which show several forecasts of vehicle populations, and the GHG emissions reductions associated with each scenario. There are separate forecasts for 2013, 2020, and 2030. These tables can be used to calculate a value of GHG reductions per car for each of the years. See Tables 4, 8, and 12 for vehicle populations, and Tables 6, 10 and 14 for GHG reductions. In 2030, the approximate annual GHG emissions for a Battery Electric Vehicle are 2.34 tons/car/year, and for a typical Plug-in Hybrid Electric Vehicle the value is 2.59 tons/car/year. For simplicity, the text above assumes a typical value of 2.5 tons/car/year over the vehicle’s lifetime. Per Phase 1 of the TEA study, the typical electric vehicle is expected to last 20 years. Therefore, the average electric vehicle will abate 50 tons of GHG over its lifetime. However, consistent with the previous section, these GHG benefits should be discounted. Assuming a discount rate of 10%, the 50 nominal tons become 23.5 “present value” tons in 2014. 20

Since the TEA study expresses the net benefits of electric vehicles on a per-vehicle basis over the lifetime of the vehicle, it is appropriate to divide the net benefits by the expected GHG reductions over the vehicle’s lifetime.

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Table 2: Comparison of Considerations in the 50% RPS Study and the TEA Study

Legend

The studies are aligned.

It is unclear whether the studies are aligned.

The studies are not aligned.

Consideration 50% RPS Study TEA Study Implications

Choice of Cost Test

The 50% RPS study does not identify a specific cost test, but it does identify the costs that it accounts for. The considerations are consistent with a Total Resource Cost test.21

Various cost tests presented within the paper, including the Total Resource Cost test.

For this consideration, the two studies are aligned. Both papers make the Total Resource Cost available.

21

“The total cost includes the cost of procuring and operating the renewable and thermal resources considered in this study, the cost of transmission and distribution system investments needed to deliver the renewable energy to loads, and non-study-related costs such as the cost of the existing grid.” 50% RPS Study, page 19.

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Consideration 50% RPS Study TEA Study Implications

Counterfactual Assumptions

(Energy prices, capacity values, etc)

Counterfactuals come from a variety sources, including energy prices established by simulation modeling, at the sub-hourly level, of a single year of prices. In total, this utilizes a single year of counterfactual assumptions.

Counterfactuals come from a variety of sources, including simulation modeling. Most prices appear to be hourly. In total, this utilizes counterfactual assumptions for the entire period 2014 – 2030.

The studies do not provide enough information to determine whether both studies utilize the same values for their counterfactuals. For example, although both studies utilize power prices that come out of simulations run on the REFLEX platform, it is unclear whether they both use the same energy values in 2030.

Monetized Externalities

The 50% RPS study does not monetize or account for any externalities (the value of reduced particulate emissions such as SOX and NOX are explicitly excluded).

Depends on the choice of cost test. The Societal Cost Test assigns a “social value” to reductions in GHG and particulate emissions; the Total Resource Cost test only includes the GHG benefits associated with Cap and Trade, which this paper excluded for the purpose of comparison with the TEA study.

These studies were not aligned, as the 50% RPS study did not monetize the value of avoided GHG, while the TEA study did. However, the TEA study provides enough information to approximate the value that the TEA study assigned to avoided, and thereby estimate the net cost of abatement.

Discounting Abated GHG

This study does not discount GHG. It simply presents the carbon impact as the GHG reduced by the higher RPS in 2030.

The simplified abatement calculations in this paper discount GHG at a 10% rate.

The studies are not aligned because the RPS study did not provide enough information to calculate the net costs of the RPS over the program’s lifetime.

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Consideration 50% RPS Study TEA Study Implications

Analysis Timeframe

This study only examines the single-year costs and benefits that occur in 2030. This is not consistent with the lifetime.

This analysis calculates the present value of net benefits for the entire period 2014-2030.

These studies are not aligned because they consider different timeframes. This drives a fundamental difference between the evaluation frameworks which means that the two studies are incomparable.

Direct vs Lifecycle Emissions

The 50% RPS study estimates direct emissions reductions. It estimates the reduced GHG from power plants due to increased levels of renewable energy. The study does not estimate the upstream GHG impacts of the renewable energy, such as a reduction in natural gas refining or the emissions associated with building the renewable facilities.

The TEA study estimates direct emissions reductions due to electric vehicles. It calculates both the increased GHG due to higher electricity use, and also the reduced GHG due to abated gasoline use. The study does not estimate the upstream GHG impacts such as a reduction in petroleum refining.

The studies are aligned in their approach to calculating GHG reductions.

GHG Reductions Attribution to Indirect

Incentives

Not applicable, as there are no indirect incentives for renewable energy.

Not applicable, as this analysis does not contemplate indirect incentives (e.g., public awareness campaigns), but rather direct incentives (e.g., the federal tax credit for electric vehicles.

The studies are aligned in their approach because neither deals with indirect incentives.

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Consideration 50% RPS Study TEA Study Implications

Effect of Large-Scale Measures on the Grid

This study accounts for the grid impacts of renewable energy. This includes transmission and distribution upgrades as well as the resources’ impacts on energy prices.

The study accounts for the impact of electric vehicles on the grid by estimating the necessary upgrades to the distribution system.

The studies are aligned because they both take into account the effect of the measures on the grid.

Sequencing of Abatement Measures

Not applicable Not applicable The studies are aligned because the sequencing of the measures is not applicable in either scenario.

As this table demonstrates, it is not appropriate to compare the abatement cost numbers that come out of these studies. While each of the

studies stands on its own and makes reasonable assumptions, the two studies are built evaluation frameworks that are fundamentally different.

Most notably, the two studies have very different analysis timeframes. The RPS study is a snapshot of a single year’s benefits and costs, while

the TEA study evaluates the benefits over a fifteen-year time period. For this reason alone, the two studies are investigating fundamentally

different questions.

Additionally, we note that for several of these categories, it was not easy to determine whether the two studies relied on the assumptions. For

that reason, we recommend that studies which could reasonably be used to calculate the cost of abatement should include such a calculation in

their paper.

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Conclusion

In order to produce a useful and fair comparison across various GHG abatement measures, there are

many considerations to be accounted for. This paper presents a list of such considerations for use in

comparing the cost-effectiveness of GHG reduction measures. A fair comparison enables policymakers

and program administrators to implement programs in the most cost-effective manner and make

informed tradeoffs between measures.

Additionally, because there are multiple approaches to calculate cost-effectiveness, it is important to

always clarify the underlying assumptions in a study, and provide an explicit abatement cost calculation

rather than leaving it to the audience. As we have demonstrated, a difference in fundamental

assumptions can make two analyses totally incomparable.

Acknowledgements

Thanks to Carl Silsbee, Abraham Cass and Geoff Burmeister for their insight and review. Any errors or

oversights are the authors’.